\(Ps\)  refers to the probability of an agent changing strategy, where \(a1s\)  refers to the observing agents satisfaction and \(a2s\)  refers to the satisfaction of the agent being observed. The \(\beta \)  value determines the selection pressure and was set to 1 unless specified otherwise. The results are normalised to give a value between 0 and 1. Learning is thus payoff-biased [1]}, with strategies giving higher individual satisfaction more likely to spread in the population. Agents that move from a social strategy to the selfish strategy retain their accumulated social capital. Pseudo-code for the simulation procedure is given in Algorithm .
